forked from PaddlePaddle/Paddle
-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathsgd_compute.cc
More file actions
77 lines (65 loc) · 2.74 KB
/
Copy pathsgd_compute.cc
File metadata and controls
77 lines (65 loc) · 2.74 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/operators/jit/kernels.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace x86 {
template <typename T>
class SGDCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
public:
using param_t = operators::ActivationParam;
void Run() override {
auto &context = ctx_->As<X86Context>();
auto &sgd_param = *param_.get_mutable<operators::SGDParam>();
CHECK(context.x86_device_context());
// param.Out->template mutable_data<T>();
const auto *param = &sgd_param.Param->raw_tensor();
const auto *grad = &sgd_param.Grad->raw_tensor();
const auto *learning_rate = &sgd_param.LearningRate->raw_tensor();
auto *param_out = &sgd_param.ParamOut->raw_tensor();
auto sz = param_out->numel();
PADDLE_ENFORCE_EQ(param->numel(), sz);
PADDLE_ENFORCE_EQ(grad->numel(), sz);
paddle::operators::jit::sgd_attr_t attr(1, sz, 1, sz, 1);
const T *lr = learning_rate->template data<T>();
const T *param_data = param->template data<T>();
const T *grad_data = grad->template data<T>();
int64_t rows_idx = 0;
T *out_data = param_out->template mutable_data<T>(
context.x86_device_context()->GetPlace());
auto sgd =
paddle::operators::jit::KernelFuncs<paddle::operators::jit::SgdTuple<T>,
platform::CPUPlace>::Cache()
.At(attr);
sgd(lr, param_data, grad_data, &rows_idx, out_data, &attr);
}
virtual ~SGDCompute() = default;
};
} // namespace x86
} // namespace kernels
} // namespace lite
} // namespace paddle
// float
REGISTER_LITE_KERNEL(sgd, kX86, kFloat, kNCHW,
paddle::lite::kernels::x86::SGDCompute<float>, def)
.BindInput("Param", {LiteType::GetTensorTy(TARGET(kX86))})
.BindInput("LearningRate", {LiteType::GetTensorTy(TARGET(kX86))})
.BindInput("Grad", {LiteType::GetTensorTy(TARGET(kX86))})
.BindOutput("ParamOut", {LiteType::GetTensorTy(TARGET(kX86))})
.Finalize();